{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YUC2GIPPNRJXBVYILTU5BCELTJ","short_pith_number":"pith:YUC2GIPP","schema_version":"1.0","canonical_sha256":"c505a321ef6c5370d7085ce9d0888b9a6d63c28055122723f9dab7a5205859ae","source":{"kind":"arxiv","id":"1901.09365","version":1},"attestation_state":"computed","paper":{"title":"Joint models as latent Gaussian models - not reinventing the wheel","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Haakon Bakka, Haavard Rue, Janet Van Niekerk","submitted_at":"2019-01-27T12:33:12Z","abstract_excerpt":"Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. Many of these resulted in new R packages and new formulations of the joint model. However, a joint model with a linear bivariate Gaussian association structure is still a latent Gaussian model (LGM) and thus can be implemented using most existing packages for LGM's. In this paper, we will show that these joint models can be implemented from a LGM viewpoint using t"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1901.09365","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-01-27T12:33:12Z","cross_cats_sorted":[],"title_canon_sha256":"23c62c62e2172c0ddaa1f2187756c730a1aab38c5278b94cbcda9f2ec8ba5c00","abstract_canon_sha256":"5a31ea5ece065e2a24b0858958b682613f3cc61d28599354c00b1d40bcf62fbc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:24.988837Z","signature_b64":"7BtqjCrnCthRoHI+Ll4F6D8yjToafiMQ7Qft/Y0ghEyumCZlNrj1ckBC0iUfN26VvlqB53+p7ALtmiKgENNqDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c505a321ef6c5370d7085ce9d0888b9a6d63c28055122723f9dab7a5205859ae","last_reissued_at":"2026-05-17T23:55:24.988393Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:24.988393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint models as latent Gaussian models - not reinventing the wheel","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Haakon Bakka, Haavard Rue, Janet Van Niekerk","submitted_at":"2019-01-27T12:33:12Z","abstract_excerpt":"Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. Many of these resulted in new R packages and new formulations of the joint model. However, a joint model with a linear bivariate Gaussian association structure is still a latent Gaussian model (LGM) and thus can be implemented using most existing packages for LGM's. In this paper, we will show that these joint models can be implemented from a LGM viewpoint using t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09365","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1901.09365","created_at":"2026-05-17T23:55:24.988471+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09365v1","created_at":"2026-05-17T23:55:24.988471+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09365","created_at":"2026-05-17T23:55:24.988471+00:00"},{"alias_kind":"pith_short_12","alias_value":"YUC2GIPPNRJX","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YUC2GIPPNRJXBVYI","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YUC2GIPP","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ","json":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ.json","graph_json":"https://pith.science/api/pith-number/YUC2GIPPNRJXBVYILTU5BCELTJ/graph.json","events_json":"https://pith.science/api/pith-number/YUC2GIPPNRJXBVYILTU5BCELTJ/events.json","paper":"https://pith.science/paper/YUC2GIPP"},"agent_actions":{"view_html":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ","download_json":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ.json","view_paper":"https://pith.science/paper/YUC2GIPP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09365&json=true","fetch_graph":"https://pith.science/api/pith-number/YUC2GIPPNRJXBVYILTU5BCELTJ/graph.json","fetch_events":"https://pith.science/api/pith-number/YUC2GIPPNRJXBVYILTU5BCELTJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ/action/storage_attestation","attest_author":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ/action/author_attestation","sign_citation":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ/action/citation_signature","submit_replication":"https://pith.science/pith/YUC2GIPPNRJXBVYILTU5BCELTJ/action/replication_record"}},"created_at":"2026-05-17T23:55:24.988471+00:00","updated_at":"2026-05-17T23:55:24.988471+00:00"}